Below you find for each supplied fasta file an individual tab. Each tab contains all the results and explanations to help you identify the possible phages.
Blue tabs also group the results. All the citations can be found in the results directory as a .bib file.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
The “UpSet” plot is an alternative but more detailed Venn diagram, and it summarizes the prediction performance of each tool for your sample.
The total amount of identified phage-contigs per tool is shown in blue bars on the left.
Black bars visualize the number of contigs that each tool or tool combination has uniquely identified, and each tool combination is shown below each black bar as a dot matrix. E.g., a black bar with two dots below it means that these two tools identified the same contigs as phages.
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.html
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contig_IDs_of_interest -folder
# Copy the input_fasta to the contig_IDs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value |
|---|---|---|
| deepvirfinder | p-value: 0 to 1 | 0 to 1 |
| metaphinder | string: phage | 1 |
| metaphinder own | string: phage | 1 |
| phigaro | score: 0 to 1 | 0 to 1 |
| pprmeta | phage_score: 0 to 1 | 0 to 1 |
| seeker | score: 0 to 1 | 0 to 1 |
| sourmash | similarity: 0 to 1 | 0 to 1 |
| vibrant | prediction: virus | 1 |
| vibrant-virome | prediction: virus | 1 |
| virfinder | p-value: 0 to 1 | 0 to 1 |
| virnet | score: 0 to 1 | 0 to 1 |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 |
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.